Knowledge elicitation for fault diagnostics in plastic injection moulding
ǂa ǂcase for machine-to-machine communication
Povzetek
In most manufacturing processes the defect rate is very low. Sometimes, only a few parts per million are defective because of a faulty process. For this reason, fault diagnostics is faced with extremely imbalanced data sets and requires large volumes of data to achieve a reasonable performance. This paper explores whether a machine-to-machine approach can be used, in which several work systems share the process data to improve the accuracy of the fault-detection model. The model is based on machine learning and is applied to industrial data from approximately two million process cycles performed on several injection moulding work systems.
Ključne besede
manufacturing system;predictive model;machine-to-machine;
Podatki
| Jezik: | Angleški jezik |
|---|---|
| Leto izida: | 2017 |
| Tipologija: | 1.01 - Izvirni znanstveni članek |
| Organizacija: | UL FS - Fakulteta za strojništvo |
| UDK: | 658.5(045) |
| COBISS: |
15490587
|
| ISSN: | 0007-8506 |
| Št. ogledov: | 1036 |
| Št. prenosov: | 429 |
| Ocena: | 0 (0 glasov) |
| Metapodatki: |
|
Ostali podatki
| Sekundarni jezik: | Slovenski jezik |
|---|---|
| Sekundarne ključne besede: | proizvodni sistemi;napovedni modeli;komunikacija stroj-stroj; |
| Konec prepovedi (OpenAIRE): | 2020-06-18 |
| Strani: | str. 433-436 |
| Letnik: | ǂVol. ǂ66 |
| Zvezek: | ǂiss. ǂ1 |
| Čas izdaje: | 2017 |
| DOI: | 10.1016/j.cirp.2017.04.001 |
| ID: | 10941837 |
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